Polars is ideal for scenarios where you need to perform large-scale data manipulations and analysis in-memory efficiently. For example, if you are working with a large dataset that requires complex transformations and you want to take full advantage of lazy evaluations to optimize performance, Polars would be a better choice. Additionally, if you are familiar with DataFrame-like APIs from Python or Pandas, Polars provides a similar interface for ease of use.
DuckDB is particularly useful when you need to perform complex SQL queries on larger datasets that don't fit into memory. For instance, if you're working on a data analysis project with a sizable dataset stored in a CSV or Parquet format, and you want to run aggregate functions or joins seamlessly, DuckDB would be more suitable than Polars, which operates primarily in-memory.